固定翼无人机用视觉+自适应控制实现精准追踪与命中。
Autonomous Tracking and Terminal Guidance of Moving Targets for Fixed-Wing UAVs
- 三阶段控制：视觉捕获、基于NMPC的跟踪、末段导引。
- 融合YOLO检测与惯性数据，实现鲁棒目标状态估计。
- 引入CBF防止自遮挡，适合高动态无人机追踪任务。
本研究提出一种针对配备云台相机的固定翼无人飞行器（UAV）的统一控制框架，实现从目标初始探测到精确终端打击的端到端任务。系统采用三阶段策略：基于视觉的目标获取、基于非线性模型预测控制（NMPC）的跟踪、以及末段导引。跟踪阶段利用无迹卡尔曼滤波（UKF）融合基于YOLO的视觉检测与惯性测量，实现未知动态下的鲁棒目标状态估计。为确保持续视觉接触，引入考虑约束的非线性模型预测控制策略，结合控制屏障函数（CBFs）显式防止无人机自遮挡——这是固定翼追踪中的常见限制。当满足终端交战条件时，系统无缝切换至基于四元数的偏置比例导航制导（BPNG）律，强制执行精确的撞击角度约束。高保真仿真表明，该框架在严格遵守飞行器动力学极限和摄像机视场约束的前提下，实现了稳定、鲁棒的跟踪与精准的终端拦截。
原文摘要 · Abstract (English)
This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual detections with inertial measurements, enabling robust target state estimation under unknown dynamics. To ensure reliable visual contact, we introduce a constraint-aware Nonlinear Model Predictive Control (NMPC) strategy that incorporates Control Barrier Functions (CBFs) to explicitly prevent UAV self-occlusion -- a common limitation in fixed-wing tracking. Upon satisfying terminal engagement conditions, the system seamlessly transitions control to a quaternion-based Biased Proportional Navigation Guidance (BPNG) law, enforcing precise impact angle constraints. High-fidelity simulations demonstrate that the framework achieves stable, robust tracking and accurate terminal interception while strictly respecting the vehicle's dynamic limits and camera field-of-view constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。